-------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\miles\Dropbox\Correlates of Political Violence\Replication Files\log.log
  log type:  text
 opened on:   8 Mar 2022, 13:45:10

. do "C:\Users\miles\AppData\Local\Temp\STD00000000.tmp"

. set more off

. import delimited "Data, February 2020.csv"
(115 vars, 1,504 obs)

. ********************************************************************************
. 
. ****
. ** Recode and create variables
. ****
. 
. * Attention checks
. gen pass1 = 0

. replace pass1 = 1 if q5 == 3
(1,458 real changes made)

. 
. gen pass2 = 0

. replace pass2 = 1 if q28 == 2
(1,389 real changes made)

. 
. keep if pass1 == 1 & pass2 == 1
(115 observations deleted)

. 
. 
. * Time spent completing survey
. gen short = 0

. replace short = 1 if durationinseconds < 420
(386 real changes made)

. 
. drop if short == 1
(386 observations deleted)

. 
. 
. * Egocentric victimhood
. foreach v of var q2_1-q2_4{ 
  2.         replace `v' = (`v' * -1) + 6
  3. }
(741 real changes made)
(799 real changes made)
(755 real changes made)
(745 real changes made)

. 
. alpha q2_1-q2_4, gen(egocentric)

Test scale = mean(unstandardized items)

Average interitem covariance:     1.040242
Number of items in the scale:            4
Scale reliability coefficient:      0.8853

. 
. egen egocentric_sd = rowsd(q2_1-q2_4)

. 
. 
. * Systemic victimhood
. foreach v of var q3_1-q3_4{ 
  2.         replace `v' = (`v' * -1) + 6
  3. }
(748 real changes made)
(784 real changes made)
(818 real changes made)
(774 real changes made)

. 
. alpha q3_1-q3_4, gen(systemic)

Test scale = mean(unstandardized items)

Average interitem covariance:     .8946837
Number of items in the scale:            4
Scale reliability coefficient:      0.8322

. 
. egen systemic_sd = rowsd(q3_1-q3_4)

. 
. * Anxiety
. alpha q6_1-q6_7, gen(anxiety)

Test scale = mean(unstandardized items)

Average interitem covariance:     .6383027
Number of items in the scale:            7
Scale reliability coefficient:      0.9367

. 
. egen anxiety_sd = rowsd(q6_1-q6_7)

. 
. 
. * Powerlessness
. foreach v of var q7_1-q7_2{ 
  2.         replace `v' = (`v' * -1) + 6
  3. }
(688 real changes made)
(743 real changes made)

. alpha q7_1-q7_2, gen(powerless)

Test scale = mean(unstandardized items)

Average interitem covariance:     .4961635
Number of items in the scale:            2
Scale reliability coefficient:      0.7817

. 
. egen powerless_sd = rowsd(q7_1-q7_2)
(4 missing values generated)

. 
. 
. * Conspiracy thinking
. foreach v of var q9_1-q9_4{ 
  2.         replace `v' = (`v' * -1) + 6
  3. }
(764 real changes made)
(699 real changes made)
(771 real changes made)
(717 real changes made)

. 
. alpha q9_1-q9_4, gen(conthink)

Test scale = mean(unstandardized items)

Average interitem covariance:     .7437345
Number of items in the scale:            4
Scale reliability coefficient:      0.8388

. 
. egen conthink_sd = rowsd(q9_1-q9_4)

. 
. 
. * How many in government are corrupt?
. gen corrupt = (q10 * -1) + 6
(70 missing values generated)

. replace corrupt = . if corrupt < 1
(0 real changes made)

. 
. 
. * Trust in government
. gen trustgov = q11
(46 missing values generated)

. replace trustgov = . if q11 > 5
(0 real changes made)

. 
. 
. * Racial resentment
. foreach v of var q14_1 q14_4{ 
  2.         replace `v' = (`v' * -1) + 6
  3. }
(761 real changes made)
(728 real changes made)

. 
. alpha q14_1-q14_4, gen(raceresent)

Test scale = mean(unstandardized items)

Average interitem covariance:     .8678363
Number of items in the scale:            4
Scale reliability coefficient:      0.7880

. 
. egen raceresent_sd = rowsd(q14_1-q14_4)

. 
. 
. * Populism
. foreach v of var q15_1-q15_9{ 
  2.         replace `v' = (`v' * -1) + 6
  3. }
(806 real changes made)
(784 real changes made)
(682 real changes made)
(793 real changes made)
(709 real changes made)
(691 real changes made)
(755 real changes made)
(790 real changes made)
(698 real changes made)

. 
. factor q15_1-q15_9, ipf
(obs=1,003)

Factor analysis/correlation                      Number of obs    =      1,003
    Method: iterated principal factors           Retained factors =          8
    Rotation: (unrotated)                        Number of params =         36

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      3.21715      2.37314            0.6705       0.6705
        Factor2  |      0.84401      0.57708            0.1759       0.8464
        Factor3  |      0.26693      0.02813            0.0556       0.9021
        Factor4  |      0.23880      0.09695            0.0498       0.9519
        Factor5  |      0.14185      0.07668            0.0296       0.9814
        Factor6  |      0.06518      0.04801            0.0136       0.9950
        Factor7  |      0.01717      0.01008            0.0036       0.9986
        Factor8  |      0.00709      0.00736            0.0015       1.0001
        Factor9  |     -0.00027            .           -0.0001       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(36) = 2507.57 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    -------------------------------------------------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4   Factor5   Factor6   Factor7   Factor8 |   Uniqueness 
    -------------+--------------------------------------------------------------------------------+--------------
           q15_1 |   0.5980    0.1636   -0.0333   -0.2952    0.0202    0.0965    0.0554   -0.0291 |      0.5138  
           q15_2 |   0.6184    0.3282    0.2116   -0.0728   -0.1545   -0.0478   -0.0549    0.0135 |      0.4304  
           q15_3 |   0.6874    0.1515   -0.0173   -0.1338    0.2272   -0.0995   -0.0346    0.0033 |      0.4236  
           q15_4 |   0.7115    0.0885   -0.3068    0.0168   -0.1807    0.0230    0.0049    0.0284 |      0.3575  
           q15_5 |   0.6979    0.1924   -0.1466    0.3106    0.1060   -0.0048    0.0067   -0.0192 |      0.3463  
           q15_6 |   0.6120   -0.4093    0.0935    0.0634   -0.0505    0.0901   -0.0567   -0.0408 |      0.4297  
           q15_7 |   0.5608   -0.4823    0.0735   -0.0151    0.1018    0.0619    0.0108    0.0529 |      0.4302  
           q15_8 |  -0.0080    0.4722    0.2068    0.1338    0.0561    0.1393    0.0114    0.0185 |      0.6932  
           q15_9 |   0.5677   -0.1364    0.2196    0.0978   -0.0772   -0.1084    0.0797   -0.0080 |      0.5772  
    -------------------------------------------------------------------------------------------------------------

. factor q15_1-q15_7 q15_9, ipf
(obs=1,003)

Factor analysis/correlation                      Number of obs    =      1,003
    Method: iterated principal factors           Retained factors =          7
    Rotation: (unrotated)                        Number of params =         28

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      3.20063      2.54782            0.7301       0.7301
        Factor2  |      0.65282      0.43719            0.1489       0.8791
        Factor3  |      0.21563      0.03679            0.0492       0.9282
        Factor4  |      0.17884      0.06522            0.0408       0.9690
        Factor5  |      0.11361      0.09406            0.0259       0.9950
        Factor6  |      0.01956      0.01679            0.0045       0.9994
        Factor7  |      0.00276      0.00299            0.0006       1.0001
        Factor8  |     -0.00023            .           -0.0001       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(28) = 2383.82 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4   Factor5   Factor6   Factor7 |   Uniqueness 
    -------------+----------------------------------------------------------------------+--------------
           q15_1 |   0.6012   -0.2051    0.2345   -0.1649    0.0909    0.0253    0.0274 |      0.5047  
           q15_2 |   0.6113   -0.2817    0.1404    0.2151    0.0619   -0.0402   -0.0224 |      0.4750  
           q15_3 |   0.6892   -0.1770    0.1020   -0.0940   -0.2246   -0.0232   -0.0084 |      0.4233  
           q15_4 |   0.7027   -0.1488   -0.1914   -0.1152    0.1748    0.0299   -0.0187 |      0.4025  
           q15_5 |   0.6880   -0.1669   -0.2990    0.0246   -0.1037   -0.0054    0.0172 |      0.3978  
           q15_6 |   0.6146    0.4235   -0.0140    0.0114    0.0811   -0.0932    0.0137 |      0.4271  
           q15_7 |   0.5647    0.4902    0.0550   -0.1102   -0.0548    0.0404   -0.0208 |      0.4206  
           q15_9 |   0.5717    0.1746    0.0362    0.2653   -0.0141    0.0743    0.0132 |      0.5652  
    ---------------------------------------------------------------------------------------------------

. alpha q15_1-q15_7 q15_9, gen(populism)

Test scale = mean(unstandardized items)

Average interitem covariance:     .5238863
Number of items in the scale:            8
Scale reliability coefficient:      0.8271

. 
. egen populism_sd = rowsd(q15_1-q15_7 q15_9)

. 
. 
. * Authoritarianism
. foreach v of var q16_1-q16_3{ 
  2.         replace `v' = (`v' * -1) + 6
  3. }
(679 real changes made)
(710 real changes made)
(680 real changes made)

. 
. *gen authoritarian = q16_2
. alpha q16_1-q16_3, gen(authoritarian)

Test scale = mean(unstandardized items)

Average interitem covariance:      .526944
Number of items in the scale:            3
Scale reliability coefficient:      0.6541

. 
. egen authoritarian_sd = rowsd(q16_1-q16_3)

. 
. 
. * Health insurance
. gen insured = q17
(2 missing values generated)

. recode insured (2=0)
(insured: 117 changes made)

. 
. gen employer = q18
(121 missing values generated)

. recode employer (2=0)
(employer: 561 changes made)

. 
. 
. * Vote choice
. gen vote2020 = 0

. replace vote2020 = 1 if q19 == 1
(789 real changes made)

. 
. gen trumpvote = .
(1,003 missing values generated)

. replace trumpvote = 1 if q20 == 1
(307 real changes made)

. replace trumpvote = 0 if q20 > 1 & q20 < .
(482 real changes made)

. 
. 
. * Partisanship
. gen pid = .
(1,003 missing values generated)

. replace pid = 1 if q23 == 1
(262 real changes made)

. replace pid = 2 if q23 == 2
(132 real changes made)

. replace pid = 3 if q24 == 2
(79 real changes made)

. replace pid = 4 if q24 == 3 | q24 == 4
(150 real changes made)

. replace pid = 5 if q24 == 1
(64 real changes made)

. replace pid = 6 if q22 == 2
(89 real changes made)

. replace pid = 7 if q22 == 1
(185 real changes made)

. 
. gen pidstrength = abs(pid - 4) + 1
(42 missing values generated)

. 
. gen rep = .
(1,003 missing values generated)

. replace rep = 1 if pid > 4 & pid < .
(338 real changes made)

. replace rep = 0 if pid < 4
(473 real changes made)

. 
. gen pid3 = .
(1,003 missing values generated)

. replace pid3 = 1 if pid < 4
(473 real changes made)

. replace pid3 = 2 if pid == 4
(150 real changes made)

. replace pid3 = 3 if pid > 4 & pid < .
(338 real changes made)

. 
. 
. * Ideology
. gen ideo = q25
(139 missing values generated)

. replace ideo = . if q25 > 7
(0 real changes made)

. 
. gen ideostrength = abs(ideo - 4) + 1
(139 missing values generated)

. 
. gen conserv = .
(1,003 missing values generated)

. replace conserv = 1 if ideo > 4 & ideo < .
(316 real changes made)

. replace conserv = 0 if ideo < 4
(284 real changes made)

. 
. gen ideo3 = .
(1,003 missing values generated)

. replace ideo3 = 1 if ideo < 4
(284 real changes made)

. replace ideo3 = 2 if ideo == 4
(264 real changes made)

. replace ideo3 = 3 if ideo > 4 & ideo < .
(316 real changes made)

. 
. 
. * Interest in politics
. gen interest = (q26 * -1) + 5
(33 missing values generated)

. 
. 
. * Trump thermometer
. gen trumpft = q27_3

. 
. 
. 
. 
. * Capitol riots and violence
. gen justified = (q30_1 * -1) + 6
(7 missing values generated)

. 
. foreach v of var q30_2-q30_4{ 
  2.         replace `v' = (`v' * -1) + 6
  3. }
(879 real changes made)
(865 real changes made)
(812 real changes made)

. 
. alpha q30_2-q30_4, gen(violence)

Test scale = mean(unstandardized items)

Average interitem covariance:     1.037583
Number of items in the scale:            3
Scale reliability coefficient:      0.8566

. 
. egen violence_sd = rowsd(q30_2-q30_4)
(1 missing value generated)

. 
. 
. * Region
. gen south = 0

. replace south = 1 if q32 == 3
(368 real changes made)

. 
. 
. * Gender
. gen female = 0

. replace female = 1 if q33 == 2
(530 real changes made)

. 
. 
. * Latinx
. gen latinx = 0

. replace latinx = 1 if q34 == 1
(109 real changes made)

. 
. 
. * Race
. gen race = .
(1,003 missing values generated)

. replace race = 1 if q35_5 == 1
(817 real changes made)

. replace race = 2 if q35_3 == 1
(127 real changes made)

. replace race = 3 if q35_2 == 1
(67 real changes made)

. replace race = 4 if q35_1 == 1
(28 real changes made)

. replace race = 4 if q35_4 == 1
(11 real changes made)

. 
. gen white = 0

. replace white = 1 if q35_5 == 1
(817 real changes made)

. 
. gen black = 0

. replace black = 1 if q35_3 == 1
(127 real changes made)

. 
. 
. * Education
. gen edu = q36

. 
. gen college = .
(1,003 missing values generated)

. replace college = 0 if edu < 4
(570 real changes made)

. replace college = 1 if edu > 3 & edu < .
(433 real changes made)

. 
. 
. * Religious denomination
. gen evangelical = 0

. replace evangelical = 1 if q38 == 1
(259 real changes made)

. 
. 
. * Religiosity
. gen attend = (q39 * -1) + 6

. 
. 
. * Military service
. gen military = 0

. replace military = 1 if q40 < 3
(145 real changes made)

. 
. 
. * Income
. gen income = q41
(41 missing values generated)

. replace income = . if q41 == 6
(0 real changes made)

. 
. 
. * Age
. gen agecat = .
(1,003 missing values generated)

. replace agecat = 1 if age >= 18 & age < 25
(125 real changes made)

. replace agecat = 2 if age >= 25 & age < 45
(344 real changes made)

. replace agecat = 3 if age >= 45 & age < 65
(338 real changes made)

. replace agecat = 4 if age >= 65 & age < .
(196 real changes made)

. 
. 
. 
. * White identity
. foreach v of var q43-q46{ 
  2.         replace `v' = (`v' * -1) + 6
  3. }
(515 real changes made)
(458 real changes made)
(509 real changes made)
(503 real changes made)

. 
. alpha q43-q46, gen(whiteidentity)

Test scale = mean(unstandardized items)

Average interitem covariance:     .7280219
Number of items in the scale:            4
Scale reliability coefficient:      0.8106

. 
. egen whiteidentity_sd = rowsd(q43-q46)
(186 missing values generated)

. 
. 
. * Rescale to range from 0 (min) to 1 (max)
. foreach v of var whiteidentity income attend ///
>         edu violence justified interest ideo pid ideostrength ///
>         pidstrength egocentric systemic age trumpft{ 
  2.         su `v', meanonly 
  3.         gen `v'2 = (`v' - r(min))/(r(max) - r(min)) 
  4. }
(186 missing values generated)
(41 missing values generated)
(1 missing value generated)
(7 missing values generated)
(33 missing values generated)
(139 missing values generated)
(42 missing values generated)
(139 missing values generated)
(42 missing values generated)

. 
. drop if violence == .
(1 observation deleted)

. 
. ********************************************************************************
. 
. ****
. ** Correlations
. ****
. 
. sem (<- violence systemic), standardize

Exogenous variables

Observed:  violence systemic

Fitting target model:

Iteration 0:   log likelihood = -2897.2627  
Iteration 1:   log likelihood = -2897.2627  

Structural equation model                       Number of obs     =      1,002
Estimation method  = ml
Log likelihood     = -2897.2627

---------------------------------------------------------------------------------------
                      |                 OIM
         Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
        mean(violence)|   1.740514   .0500967    34.74   0.000     1.642326    1.838702
        mean(systemic)|   2.558566   .0653039    39.18   0.000     2.430573    2.686559
----------------------+----------------------------------------------------------------
         var(violence)|          1          .                             .           .
         var(systemic)|          1          .                             .           .
----------------------+----------------------------------------------------------------
cov(violence,systemic)|   .3761975   .0271203    13.87   0.000     .3230427    .4293522
---------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence egocentric), standardize

Exogenous variables

Observed:  violence egocentric

Fitting target model:

Iteration 0:   log likelihood = -2953.3068  
Iteration 1:   log likelihood = -2953.3068  

Structural equation model                       Number of obs     =      1,002
Estimation method  = ml
Log likelihood     = -2953.3068

-----------------------------------------------------------------------------------------
                        |                 OIM
           Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
          mean(violence)|   1.740514   .0500967    34.74   0.000     1.642326    1.838702
        mean(egocentric)|   2.488693   .0639423    38.92   0.000     2.363368    2.614018
------------------------+----------------------------------------------------------------
           var(violence)|          1          .                             .           .
         var(egocentric)|          1          .                             .           .
------------------------+----------------------------------------------------------------
cov(violence,egocentric)|    .349982   .0277217    12.62   0.000     .2956486    .4043155
-----------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence anxiety), standardize 

Exogenous variables

Observed:  violence anxiety

Fitting target model:

Iteration 0:   log likelihood = -2731.4071  
Iteration 1:   log likelihood = -2731.4071  

Structural equation model                       Number of obs     =      1,002
Estimation method  = ml
Log likelihood     = -2731.4071

--------------------------------------------------------------------------------------
                     |                 OIM
        Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
       mean(violence)|   1.740514   .0500967    34.74   0.000     1.642326    1.838702
        mean(anxiety)|   2.226729   .0589255    37.79   0.000     2.111237    2.342221
---------------------+----------------------------------------------------------------
        var(violence)|          1          .                             .           .
         var(anxiety)|          1          .                             .           .
---------------------+----------------------------------------------------------------
cov(violence,anxiety)|   .1700373   .0306778     5.54   0.000     .1099099    .2301647
--------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence powerless), standardize 
(2 observations with missing values excluded)

Exogenous variables

Observed:  violence powerless

Fitting target model:

Iteration 0:   log likelihood =  -2693.348  
Iteration 1:   log likelihood =  -2693.348  

Structural equation model                       Number of obs     =      1,000
Estimation method  = ml
Log likelihood     =  -2693.348

----------------------------------------------------------------------------------------
                       |                 OIM
          Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
         mean(violence)|   1.739953    .050137    34.70   0.000     1.641686    1.838219
        mean(powerless)|   2.900104   .0721478    40.20   0.000     2.758697    3.041511
-----------------------+----------------------------------------------------------------
          var(violence)|          1          .                             .           .
         var(powerless)|          1          .                             .           .
-----------------------+----------------------------------------------------------------
cov(violence,powerless)|    .125194   .0311271     4.02   0.000      .064186    .1862021
----------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence populism), standardize

Exogenous variables

Observed:  violence populism

Fitting target model:

Iteration 0:   log likelihood = -2649.7126  
Iteration 1:   log likelihood = -2649.7126  

Structural equation model                       Number of obs     =      1,002
Estimation method  = ml
Log likelihood     = -2649.7126

---------------------------------------------------------------------------------------
                      |                 OIM
         Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
        mean(violence)|   1.740514   .0500967    34.74   0.000     1.642326    1.838702
        mean(populism)|   4.193786   .0988654    42.42   0.000     4.000013    4.387559
----------------------+----------------------------------------------------------------
         var(violence)|          1          .                             .           .
         var(populism)|          1          .                             .           .
----------------------+----------------------------------------------------------------
cov(violence,populism)|   .3314877   .0281198    11.79   0.000     .2763739    .3866016
---------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence authoritarian), standardize

Exogenous variables

Observed:  violence authoritarian

Fitting target model:

Iteration 0:   log likelihood = -2767.1955  
Iteration 1:   log likelihood = -2767.1955  

Structural equation model                       Number of obs     =      1,002
Estimation method  = ml
Log likelihood     = -2767.1955

--------------------------------------------------------------------------------------------
                           |                 OIM
              Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
             mean(violence)|   1.740514   .0500967    34.74   0.000     1.642326    1.838702
        mean(authoritarian)|   3.247195   .0791177    41.04   0.000     3.092127    3.402262
---------------------------+----------------------------------------------------------------
              var(violence)|          1          .                             .           .
         var(authoritarian)|          1          .                             .           .
---------------------------+----------------------------------------------------------------
cov(violence,authoritarian)|    .341255   .0279122    12.23   0.000      .286548     .395962
--------------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence conthink), standardize 

Exogenous variables

Observed:  violence conthink

Fitting target model:

Iteration 0:   log likelihood = -2848.7627  
Iteration 1:   log likelihood = -2848.7627  

Structural equation model                       Number of obs     =      1,002
Estimation method  = ml
Log likelihood     = -2848.7627

---------------------------------------------------------------------------------------
                      |                 OIM
         Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
        mean(violence)|   1.740514   .0500967    34.74   0.000     1.642326    1.838702
        mean(conthink)|    3.66712   .0877979    41.77   0.000     3.495039    3.839201
----------------------+----------------------------------------------------------------
         var(violence)|          1          .                             .           .
         var(conthink)|          1          .                             .           .
----------------------+----------------------------------------------------------------
cov(violence,conthink)|    .235661   .0298367     7.90   0.000      .177182    .2941399
---------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence corrupt), standardize 
(70 observations with missing values excluded)

Exogenous variables

Observed:  violence corrupt

Fitting target model:

Iteration 0:   log likelihood = -2700.2687  
Iteration 1:   log likelihood = -2700.2687  

Structural equation model                       Number of obs     =        932
Estimation method  = ml
Log likelihood     = -2700.2687

--------------------------------------------------------------------------------------
                     |                 OIM
        Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
       mean(violence)|   1.724026   .0516481    33.38   0.000     1.622797    1.825254
        mean(corrupt)|    3.22869   .0816423    39.55   0.000     3.068674    3.388706
---------------------+----------------------------------------------------------------
        var(violence)|          1          .                             .           .
         var(corrupt)|          1          .                             .           .
---------------------+----------------------------------------------------------------
cov(violence,corrupt)|   .2057465   .0313695     6.56   0.000     .1442634    .2672295
--------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence trust), standardize 
(46 observations with missing values excluded)

Exogenous variables

Observed:  violence trustgov

Fitting target model:

Iteration 0:   log likelihood = -2793.5008  
Iteration 1:   log likelihood = -2793.5008  

Structural equation model                       Number of obs     =        956
Estimation method  = ml
Log likelihood     = -2793.5008

---------------------------------------------------------------------------------------
                      |                 OIM
         Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
        mean(violence)|   1.730385   .0511082    33.86   0.000     1.630214    1.830555
        mean(trustgov)|   2.506024   .0658075    38.08   0.000     2.377044    2.635004
----------------------+----------------------------------------------------------------
         var(violence)|          1          .                             .           .
         var(trustgov)|          1          .                             .           .
----------------------+----------------------------------------------------------------
cov(violence,trustgov)|   .0124182   .0323373     0.38   0.701    -.0509618    .0757982
---------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence interest), standardize 
(33 observations with missing values excluded)

Exogenous variables

Observed:  violence interest

Fitting target model:

Iteration 0:   log likelihood = -2787.1824  
Iteration 1:   log likelihood = -2787.1824  

Structural equation model                       Number of obs     =        969
Estimation method  = ml
Log likelihood     = -2787.1824

---------------------------------------------------------------------------------------
                      |                 OIM
         Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
        mean(violence)|    1.73985   .0509309    34.16   0.000     1.640028    1.839673
        mean(interest)|   3.079964   .0769858    40.01   0.000     2.929074    3.230853
----------------------+----------------------------------------------------------------
         var(violence)|          1          .                             .           .
         var(interest)|          1          .                             .           .
----------------------+----------------------------------------------------------------
cov(violence,interest)|   .0066164   .0321232     0.21   0.837     -.056344    .0695767
---------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence whiteidentity), standardize 
(185 observations with missing values excluded)

Exogenous variables

Observed:  violence whiteidentity

Fitting target model:

Iteration 0:   log likelihood =  -2306.189  
Iteration 1:   log likelihood =  -2306.189  

Structural equation model                       Number of obs     =        817
Estimation method  = ml
Log likelihood     =  -2306.189

--------------------------------------------------------------------------------------------
                           |                 OIM
              Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
             mean(violence)|   1.708731   .0548714    31.14   0.000     1.601185    1.816277
        mean(whiteidentity)|   3.465674   .0925991    37.43   0.000     3.284183    3.647165
---------------------------+----------------------------------------------------------------
              var(violence)|          1          .                             .           .
         var(whiteidentity)|          1          .                             .           .
---------------------------+----------------------------------------------------------------
cov(violence,whiteidentity)|   .2981148   .0318763     9.35   0.000     .2356384    .3605913
--------------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence raceresent), standardize 

Exogenous variables

Observed:  violence raceresent

Fitting target model:

Iteration 0:   log likelihood = -2982.9655  
Iteration 1:   log likelihood = -2982.9655  

Structural equation model                       Number of obs     =      1,002
Estimation method  = ml
Log likelihood     = -2982.9655

-----------------------------------------------------------------------------------------
                        |                 OIM
           Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
          mean(violence)|   1.740514   .0500967    34.74   0.000     1.642326    1.838702
        mean(raceresent)|   2.805734    .070187    39.98   0.000      2.66817    2.943298
------------------------+----------------------------------------------------------------
           var(violence)|          1          .                             .           .
         var(raceresent)|          1          .                             .           .
------------------------+----------------------------------------------------------------
cov(violence,raceresent)|   .0932892   .0313163     2.98   0.003     .0319105     .154668
-----------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence ideostrength), standardize
(138 observations with missing values excluded)

Exogenous variables

Observed:  violence ideostrength

Fitting target model:

Iteration 0:   log likelihood =  -2652.108  
Iteration 1:   log likelihood =  -2652.108  

Structural equation model                       Number of obs     =        864
Estimation method  = ml
Log likelihood     =  -2652.108

-------------------------------------------------------------------------------------------
                          |                 OIM
             Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
            mean(violence)|   1.710682   .0533942    32.04   0.000     1.606031    1.815333
        mean(ideostrength)|   2.132319    .061552    34.64   0.000      2.01168    2.252959
--------------------------+----------------------------------------------------------------
             var(violence)|          1          .                             .           .
         var(ideostrength)|          1          .                             .           .
--------------------------+----------------------------------------------------------------
cov(violence,ideostrength)|   .1206579   .0335254     3.60   0.000     .0549493    .1863665
-------------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence pidstrength), standardize 
(42 observations with missing values excluded)

Exogenous variables

Observed:  violence pidstrength

Fitting target model:

Iteration 0:   log likelihood = -2912.5808  
Iteration 1:   log likelihood = -2912.5808  

Structural equation model                       Number of obs     =        960
Estimation method  = ml
Log likelihood     = -2912.5808

------------------------------------------------------------------------------------------
                         |                 OIM
            Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
           mean(violence)|   1.730508   .0510038    33.93   0.000     1.630542    1.830473
        mean(pidstrength)|   2.704738   .0696554    38.83   0.000     2.568216     2.84126
-------------------------+----------------------------------------------------------------
            var(violence)|          1          .                             .           .
         var(pidstrength)|          1          .                             .           .
-------------------------+----------------------------------------------------------------
cov(violence,pidstrength)|   .0532537   .0321833     1.65   0.098    -.0098244    .1163319
------------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence rep), standardize 
(191 observations with missing values excluded)

Exogenous variables

Observed:  violence rep

Fitting target model:

Iteration 0:   log likelihood = -1813.7773  
Iteration 1:   log likelihood = -1813.7773  

Structural equation model                       Number of obs     =        811
Estimation method  = ml
Log likelihood     = -1813.7773

----------------------------------------------------------------------------------
                 |                 OIM
    Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
   mean(violence)|   1.705564   .0550134    31.00   0.000     1.597739    1.813388
        mean(rep)|   .8453329   .0409097    20.66   0.000     .7651513    .9255145
-----------------+----------------------------------------------------------------
    var(violence)|          1          .                             .           .
         var(rep)|          1          .                             .           .
-----------------+----------------------------------------------------------------
cov(violence,rep)|   .0115808     .03511     0.33   0.742    -.0572336    .0803952
----------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence conserv), standardize 
(402 observations with missing values excluded)

Exogenous variables

Observed:  violence conserv

Fitting target model:

Iteration 0:   log likelihood = -1380.3514  
Iteration 1:   log likelihood = -1380.3514  

Structural equation model                       Number of obs     =        600
Estimation method  = ml
Log likelihood     = -1380.3514

--------------------------------------------------------------------------------------
                     |                 OIM
        Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
       mean(violence)|   1.656994   .0628863    26.35   0.000     1.533739    1.780249
        mean(conserv)|   1.054835   .0509303    20.71   0.000      .955013    1.154656
---------------------+----------------------------------------------------------------
        var(violence)|          1          .                             .           .
         var(conserv)|          1          .                             .           .
---------------------+----------------------------------------------------------------
cov(violence,conserv)|   -.060503   .0406754    -1.49   0.137    -.1402253    .0192193
--------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence attend), standardize 

Exogenous variables

Observed:  violence attend

Fitting target model:

Iteration 0:   log likelihood = -3367.5147  
Iteration 1:   log likelihood = -3367.5147  

Structural equation model                       Number of obs     =      1,002
Estimation method  = ml
Log likelihood     = -3367.5147

-------------------------------------------------------------------------------------
                    |                 OIM
       Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
      mean(violence)|   1.740514   .0500967    34.74   0.000     1.642326    1.838702
        mean(attend)|   1.597335   .0476571    33.52   0.000     1.503929    1.690741
--------------------+----------------------------------------------------------------
       var(violence)|          1          .                             .           .
         var(attend)|          1          .                             .           .
--------------------+----------------------------------------------------------------
cov(violence,attend)|   .1976132   .0303575     6.51   0.000     .1381136    .2571129
-------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence evangelical), standardize 

Exogenous variables

Observed:  violence evangelical

Fitting target model:

Iteration 0:   log likelihood = -2101.3852  
Iteration 1:   log likelihood = -2101.3852  

Structural equation model                       Number of obs     =      1,002
Estimation method  = ml
Log likelihood     = -2101.3852

------------------------------------------------------------------------------------------
                         |                 OIM
            Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
           mean(violence)|   1.740514   .0500967    34.74   0.000     1.642326    1.838702
        mean(evangelical)|   .5904124   .0342337    17.25   0.000     .5233155    .6575093
-------------------------+----------------------------------------------------------------
            var(violence)|          1          .                             .           .
         var(evangelical)|          1          .                             .           .
-------------------------+----------------------------------------------------------------
cov(violence,evangelical)|   .1405306   .0309673     4.54   0.000     .0798358    .2012254
------------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence military), standardize 

Exogenous variables

Observed:  violence military

Fitting target model:

Iteration 0:   log likelihood = -1860.7573  
Iteration 1:   log likelihood = -1860.7573  

Structural equation model                       Number of obs     =      1,002
Estimation method  = ml
Log likelihood     = -1860.7573

---------------------------------------------------------------------------------------
                      |                 OIM
         Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
        mean(violence)|   1.740514   .0500967    34.74   0.000     1.642326    1.838702
        mean(military)|    .411333   .0329003    12.50   0.000     .3468495    .4758165
----------------------+----------------------------------------------------------------
         var(violence)|          1          .                             .           .
         var(military)|          1          .                             .           .
----------------------+----------------------------------------------------------------
cov(violence,military)|   .2468856   .0296656     8.32   0.000      .188742    .3050292
---------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence income), standardize 
(41 observations with missing values excluded)

Exogenous variables

Observed:  violence income

Fitting target model:

Iteration 0:   log likelihood = -3018.6431  
Iteration 1:   log likelihood = -3018.6431  

Structural equation model                       Number of obs     =        961
Estimation method  = ml
Log likelihood     = -3018.6431

-------------------------------------------------------------------------------------
                    |                 OIM
       Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
      mean(violence)|   1.731292   .0509911    33.95   0.000     1.631352    1.831233
        mean(income)|   2.714516   .0698169    38.88   0.000     2.577678    2.851355
--------------------+----------------------------------------------------------------
       var(violence)|          1          .                             .           .
         var(income)|          1          .                             .           .
--------------------+----------------------------------------------------------------
cov(violence,income)|   .0298623   .0322293     0.93   0.354     -.033306    .0930306
-------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence insured), standardize
(2 observations with missing values excluded)

Exogenous variables

Observed:  violence insured

Fitting target model:

Iteration 0:   log likelihood = -1793.8112  
Iteration 1:   log likelihood = -1793.8112  

Structural equation model                       Number of obs     =      1,000
Estimation method  = ml
Log likelihood     = -1793.8112

--------------------------------------------------------------------------------------
                     |                 OIM
        Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
       mean(violence)|    1.74361   .0502005    34.73   0.000     1.645219    1.842001
        mean(insured)|   2.747182   .0690906    39.76   0.000     2.611767    2.882597
---------------------+----------------------------------------------------------------
        var(violence)|          1          .                             .           .
         var(insured)|          1          .                             .           .
---------------------+----------------------------------------------------------------
cov(violence,insured)|  -.0500177   .0315437    -1.59   0.113    -.1118422    .0118067
--------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence edu), standardize 

Exogenous variables

Observed:  violence edu

Fitting target model:

Iteration 0:   log likelihood = -2997.9252  
Iteration 1:   log likelihood = -2997.9252  

Structural equation model                       Number of obs     =      1,002
Estimation method  = ml
Log likelihood     = -2997.9252

----------------------------------------------------------------------------------
                 |                 OIM
    Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
   mean(violence)|   1.740514   .0500967    34.74   0.000     1.642326    1.838702
        mean(edu)|     3.1008   .0761307    40.73   0.000     2.951586    3.250013
-----------------+----------------------------------------------------------------
    var(violence)|          1          .                             .           .
         var(edu)|          1          .                             .           .
-----------------+----------------------------------------------------------------
cov(violence,edu)|   .0772459   .0314027     2.46   0.014     .0156977     .138794
----------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence female), standardize 

Exogenous variables

Observed:  violence female

Fitting target model:

Iteration 0:   log likelihood = -2219.8312  
Iteration 1:   log likelihood = -2219.8312  

Structural equation model                       Number of obs     =      1,002
Estimation method  = ml
Log likelihood     = -2219.8312

-------------------------------------------------------------------------------------
                    |                 OIM
       Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
      mean(violence)|   1.740514   .0500967    34.74   0.000     1.642326    1.838702
        mean(female)|   1.057541   .0394472    26.81   0.000      .980226    1.134856
--------------------+----------------------------------------------------------------
       var(violence)|          1          .                             .           .
         var(female)|          1          .                             .           .
--------------------+----------------------------------------------------------------
cov(violence,female)|  -.2122289   .0301683    -7.03   0.000    -.2713576   -.1531001
-------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. sem (<- violence south), standardize

Exogenous variables

Observed:  violence south

Fitting target model:

Iteration 0:   log likelihood = -2206.9166  
Iteration 1:   log likelihood = -2206.9166  

Structural equation model                       Number of obs     =      1,002
Estimation method  = ml
Log likelihood     = -2206.9166

------------------------------------------------------------------------------------
                   |                 OIM
      Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
     mean(violence)|   1.740514   .0500967    34.74   0.000     1.642326    1.838702
        mean(south)|    .760232   .0358665    21.20   0.000     .6899351     .830529
-------------------+----------------------------------------------------------------
      var(violence)|          1          .                             .           .
         var(south)|          1          .                             .           .
-------------------+----------------------------------------------------------------
cov(violence,south)|   .0277026    .031567     0.88   0.380    -.0341675    .0895727
------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0)   =      0.00, Prob > chi2 =      .

. 
. ********************************************************************************
. 
. ****
. ** Generate profile and then
. ** model attitudes about riot
. ****
. 
. gen profile = .
(1,002 missing values generated)

. replace profile = 1 if systemic <= 3.5 & populism <= 2.625 & raceresent <= 2    
(81 real changes made)

. replace profile = 2 if systemic <= 3.5 & populism <= 2.625 & raceresent > 2
(103 real changes made)

. replace profile = 3 if systemic <= 3.5 & populism > 2.625 & authoritarian <= 4 & systemic <= 2.75
(375 real changes made)

. replace profile = 4 if systemic <= 3.5 & populism > 2.625 & authoritarian <= 4 & systemic > 2.75
(227 real changes made)

. replace profile = 5 if systemic <= 3.5 & populism > 2.625 & authoritarian > 4
(35 real changes made)

. replace profile = 6 if military == 0 & systemic > 3.5 & whiteidentity <= 4.25
(75 real changes made)

. replace profile = 7 if authoritarian <= 3 & military > 0 & systemic > 3.5
(11 real changes made)

. replace profile = 8 if military == 0 & systemic > 3.5 & whiteidentity > 4.25
(63 real changes made)

. replace profile = 9 if authoritarian > 3 & military > 0 & systemic > 3.5
(32 real changes made)

. 
. su profile, meanonly 

. gen profile2 = (profile - r(min))/(r(max) - r(min)) 

. 
. hist profile
(bin=30, start=1, width=.26666667)

. corr profile justified violence
(obs=996)

             |  profile justif~d violence
-------------+---------------------------
     profile |   1.0000
   justified |   0.3976   1.0000
    violence |   0.4595   0.7325   1.0000


. 
. * Naive model
. reg justified i.pid3 i.ideo3 pidstrength2 ideostrength2 interest2 edu2 ///
>         age2 income2 female white south

      Source |       SS           df       MS      Number of obs   =       815
-------------+----------------------------------   F(13, 801)      =     14.33
       Model |  264.399884        13  20.3384526   Prob > F        =    0.0000
    Residual |  1136.54981       801  1.41891362   R-squared       =    0.1887
-------------+----------------------------------   Adj R-squared   =    0.1756
       Total |  1400.94969       814  1.72106842   Root MSE        =    1.1912

-------------------------------------------------------------------------------
    justified |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
         pid3 |
           2  |   .6668825    .205481     3.25   0.001     .2635377    1.070227
           3  |   .2751661   .1236163     2.23   0.026      .032516    .5178163
              |
        ideo3 |
           2  |    .480738   .1783636     2.70   0.007     .1306228    .8308532
           3  |   .2313877   .1330821     1.74   0.082    -.0298432    .4926187
              |
 pidstrength2 |   .7760204   .1934978     4.01   0.000     .3961978    1.155843
ideostrength2 |   .6720118   .2146563     3.13   0.002     .2506565    1.093367
    interest2 |   -.146775   .1588955    -0.92   0.356    -.4586758    .1651259
         edu2 |   .6163461   .1826671     3.37   0.001     .2577834    .9749088
         age2 |   -2.06151   .2011688   -10.25   0.000     -2.45639    -1.66663
      income2 |  -.1496147   .1568633    -0.95   0.340    -.4575264     .158297
       female |  -.3395433   .0860656    -3.95   0.000    -.5084841   -.1706024
        white |   .0850083   .1230594     0.69   0.490    -.1565487    .3265653
        south |   .0005322   .0880813     0.01   0.995    -.1723652    .1734295
        _cons |   1.367489   .2601812     5.26   0.000     .8567715    1.878207
-------------------------------------------------------------------------------

. est store mod1

. 
. * With Trump thermometer
. reg justified i.pid3 i.ideo3 pidstrength2 ideostrength2 interest2 edu2 ///
>         age2 income2 female white south trumpft2

      Source |       SS           df       MS      Number of obs   =       815
-------------+----------------------------------   F(14, 800)      =     23.39
       Model |  406.936604        14  29.0669003   Prob > F        =    0.0000
    Residual |   994.01309       800  1.24251636   R-squared       =    0.2905
-------------+----------------------------------   Adj R-squared   =    0.2781
       Total |  1400.94969       814  1.72106842   Root MSE        =    1.1147

-------------------------------------------------------------------------------
    justified |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
         pid3 |
           2  |   .2110641    .196938     1.07   0.284    -.1755122    .5976403
           3  |  -.4611447   .1345634    -3.43   0.001    -.7252838   -.1970056
              |
        ideo3 |
           2  |    .231775   .1685196     1.38   0.169    -.0990178    .5625679
           3  |  -.1025079    .128378    -0.80   0.425    -.3545054    .1494897
              |
 pidstrength2 |   .5180891   .1826655     2.84   0.005     .1595288    .8766493
ideostrength2 |    .398587   .2024865     1.97   0.049     .0011195    .7960545
    interest2 |  -.1901081   .1487461    -1.28   0.202    -.4820868    .1018706
         edu2 |   .6009407    .170942     3.52   0.000      .265393    .9364885
         age2 |  -1.862981   .1891598    -9.85   0.000    -2.234289   -1.491673
      income2 |  -.1841992   .1468248    -1.25   0.210    -.4724066    .1040082
       female |  -.3114662    .080581    -3.87   0.000    -.4696414    -.153291
        white |   .0403419   .1152318     0.35   0.726    -.1858505    .2665344
        south |   -.046913   .0825435    -0.57   0.570    -.2089404    .1151144
     trumpft2 |   1.613973   .1506898    10.71   0.000     1.318179    1.909767
        _cons |   1.625731    .244663     6.64   0.000     1.145474    2.105988
-------------------------------------------------------------------------------

. est store mod2  

. 
. * With Trump thermometer + violence profile
. reg justified i.pid3 i.ideo3 pidstrength2 ideostrength2 interest2 edu2 ///
>         age2 income2 female white south trumpft2 profile2

      Source |       SS           df       MS      Number of obs   =       815
-------------+----------------------------------   F(15, 799)      =     31.66
       Model |  522.269453        15  34.8179636   Prob > F        =    0.0000
    Residual |   878.68024       799  1.09972496   R-squared       =    0.3728
-------------+----------------------------------   Adj R-squared   =    0.3610
       Total |  1400.94969       814  1.72106842   Root MSE        =    1.0487

-------------------------------------------------------------------------------
    justified |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
         pid3 |
           2  |   .1631936   .1853355     0.88   0.379    -.2006085    .5269957
           3  |   -.431674   .1266282    -3.41   0.001    -.6802372   -.1831109
              |
        ideo3 |
           2  |    .215907   .1585485     1.36   0.174    -.0953139    .5271278
           3  |  -.0514729    .120879    -0.43   0.670    -.2887509    .1858051
              |
 pidstrength2 |    .502401    .171856     2.92   0.004     .1650584    .8397436
ideostrength2 |   .2892734   .1907953     1.52   0.130    -.0852458    .6637927
    interest2 |  -.2201873   .1399691    -1.57   0.116    -.4949378    .0545633
         edu2 |   .5120285   .1610541     3.18   0.002     .1958895    .8281676
         age2 |  -1.359323   .1846299    -7.36   0.000    -1.721739   -.9969056
      income2 |  -.0531751   .1387221    -0.38   0.702    -.3254778    .2191276
       female |   -.219759   .0763366    -2.88   0.004     -.369603    -.069915
        white |   .1518642   .1089541     1.39   0.164    -.0620059    .3657343
        south |  -.0566313   .0776616    -0.73   0.466    -.2090761    .0958136
     trumpft2 |    1.42729   .1429342     9.99   0.000     1.146719     1.70786
     profile2 |   1.659046   .1620033    10.24   0.000     1.341044    1.977048
        _cons |   .8230175   .2431559     3.38   0.001     .3457176    1.300317
-------------------------------------------------------------------------------

. est store mod3  

. 
. margins, at(trumpft2=(0(0.1)1)) 

Predictive margins                              Number of obs     =        815
Model VCE    : OLS

Expression   : Linear prediction, predict()

1._at        : trumpft2        =           0

2._at        : trumpft2        =          .1

3._at        : trumpft2        =          .2

4._at        : trumpft2        =          .3

5._at        : trumpft2        =          .4

6._at        : trumpft2        =          .5

7._at        : trumpft2        =          .6

8._at        : trumpft2        =          .7

9._at        : trumpft2        =          .8

10._at       : trumpft2        =          .9

11._at       : trumpft2        =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    1.36439   .0650614    20.97   0.000     1.236679    1.492102
          2  |   1.507119    .053872    27.98   0.000     1.401372    1.612867
          3  |   1.649848   .0444972    37.08   0.000     1.562503    1.737193
          4  |   1.792577   .0382937    46.81   0.000     1.717409    1.867746
          5  |   1.935306   .0368975    52.45   0.000     1.862879    2.007734
          6  |   2.078035    .040805    50.93   0.000     1.997938    2.158133
          7  |   2.220764   .0487573    45.55   0.000     2.125057    2.316472
          8  |   2.363493   .0591447    39.96   0.000     2.247396     2.47959
          9  |   2.506222   .0709051    35.35   0.000      2.36704    2.645404
         10  |   2.648951   .0834599    31.74   0.000     2.485124    2.812778
         11  |    2.79168   .0964997    28.93   0.000     2.602257    2.981103
------------------------------------------------------------------------------

. matrix list r(table)

r(table)[9,11]
                1.         2.         3.         4.         5.         6.         7.         8.         9.        10.        11.
              _at        _at        _at        _at        _at        _at        _at        _at        _at        _at        _at
     b  1.3643904  1.5071194  1.6498484  1.7925773  1.9353063  2.0780352  2.2207642  2.3634931  2.5062221   2.648951    2.79168
    se  .06506137  .05387195  .04449716  .03829373  .03689751  .04080498  .04875726  .05914471  .07090506  .08345994  .09649974
     t  20.970823  27.975956  37.077614  46.811248  52.450863  50.926022  45.547359  39.961193  35.346167  31.739193  28.929406
pvalue  3.902e-78  1.33e-120  7.98e-176  3.45e-231  5.65e-261  4.32e-253  2.82e-224  1.02e-192  1.66e-165  1.12e-143  1.85e-126
    ll   1.236679  1.4013721  1.5625032  1.7174091  1.8628788  1.9979376  2.1250568  2.2473958  2.3670399  2.4851244  2.6022571
    ul  1.4921018  1.6128667  1.7371935  1.8677455  2.0077338  2.1581328  2.3164717  2.4795905  2.6454043  2.8127777   2.981103
    df        799        799        799        799        799        799        799        799        799        799        799
  crit  1.9629375  1.9629375  1.9629375  1.9629375  1.9629375  1.9629375  1.9629375  1.9629375  1.9629375  1.9629375  1.9629375
 eform          0          0          0          0          0          0          0          0          0          0          0

. matrix m0 = r(at)

. matrix  m11 = m0[1..., "trumpft2"]

. matrix m1 = r(table)

. matrix  m12 = m1["b", 1...]'

. matrix m13 = m1["ll".."ul",1...]'

. matrix first = m11,m12,m13

. 
. margins, at(profile2=(0(0.1)1)) 

Predictive margins                              Number of obs     =        815
Model VCE    : OLS

Expression   : Linear prediction, predict()

1._at        : profile2        =           0

2._at        : profile2        =          .1

3._at        : profile2        =          .2

4._at        : profile2        =          .3

5._at        : profile2        =          .4

6._at        : profile2        =          .5

7._at        : profile2        =          .6

8._at        : profile2        =          .7

9._at        : profile2        =          .8

10._at       : profile2        =          .9

11._at       : profile2        =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   1.329871    .066749    19.92   0.000     1.198847    1.460895
          2  |   1.495776    .053964    27.72   0.000     1.389848    1.601704
          3  |    1.66168   .0435168    38.18   0.000      1.57626    1.747101
          4  |   1.827585   .0374194    48.84   0.000     1.754133    1.901037
          5  |    1.99349   .0378366    52.69   0.000     1.919219     2.06776
          6  |   2.159394   .0445859    48.43   0.000     2.071875    2.246914
          7  |   2.325299   .0553995    41.97   0.000     2.216553    2.434045
          8  |   2.491203   .0683755    36.43   0.000     2.356986     2.62542
          9  |   2.657108   .0824998    32.21   0.000     2.495166     2.81905
         10  |   2.823012   .0972734    29.02   0.000     2.632071    3.013954
         11  |   2.988917   .1124408    26.58   0.000     2.768203    3.209631
------------------------------------------------------------------------------

. matrix m2 = r(at)

. matrix  m21 = m2[1..., "profile2"]

. matrix m3 = r(table)

. matrix m31 = m3["b", 1...]'

. matrix m32 = m3["ll".."ul",1...]'

. matrix second = m21,m31,m32

. 
. matrix rownames first = 1

. matrix rownames second = 2

. matrix colnames first = scale estimate lower upper

. 
. matrix RESULTS = first \ second

. 
. * Save predictions
. preserve

. matsave RESULTS, dropall
data in memory will be dropped
Press any key to continue, or Break to abort
matrix RESULTS saved

. rename _rowname order

. saveold "Justified Predictions.dta", version(12) replace
(saving in Stata 12 format, which can be read by Stata 11 or 12)
file Justified Predictions.dta saved

. erase RESULTS.dta

. clear

. restore 

. 
. * With interaction between Trump thermometer and violence profile
. reg justified i.pid3 i.ideo3 pidstrength2 ideostrength2 interest2 edu2 ///
>         age2 income2 female white south c.trumpft2##c.profile2

      Source |       SS           df       MS      Number of obs   =       815
-------------+----------------------------------   F(16, 798)      =     30.46
       Model |  531.142237        16  33.1963898   Prob > F        =    0.0000
    Residual |  869.807456       798  1.08998428   R-squared       =    0.3791
-------------+----------------------------------   Adj R-squared   =    0.3667
       Total |  1400.94969       814  1.72106842   Root MSE        =     1.044

---------------------------------------------------------------------------------------
            justified |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                 pid3 |
                   2  |   .2044056   .1850775     1.10   0.270    -.1588905    .5677018
                   3  |  -.4036405   .1264484    -3.19   0.001    -.6518513   -.1554296
                      |
                ideo3 |
                   2  |   .2340767   .1579732     1.48   0.139    -.0760155    .5441688
                   3  |   .0049902   .1219589     0.04   0.967    -.2344079    .2443883
                      |
         pidstrength2 |   .5215625    .171225     3.05   0.002     .1854579    .8576671
        ideostrength2 |   .2922863   .1899514     1.54   0.124    -.0805771    .6651497
            interest2 |  -.2482448   .1396944    -1.78   0.076    -.5224567    .0259671
                 edu2 |   .4755009   .1608495     2.96   0.003     .1597627    .7912391
                 age2 |  -1.415929    .184878    -7.66   0.000    -1.778833   -1.053024
              income2 |  -.0703227    .138237    -0.51   0.611    -.3416739    .2010285
               female |  -.2307105   .0760947    -3.03   0.003    -.3800798   -.0813411
                white |   .1358222   .1086161     1.25   0.211    -.0773849    .3490293
                south |  -.0503557   .0773482    -0.65   0.515    -.2021856    .1014742
             trumpft2 |   .9476743   .2202443     4.30   0.000     .5153478    1.380001
             profile2 |   1.236705   .2189175     5.65   0.000     .8069824    1.666427
                      |
c.trumpft2#c.profile2 |   1.214705   .4257464     2.85   0.004     .3789898     2.05042
                      |
                _cons |   1.014358   .2511945     4.04   0.000     .5212783    1.507438
---------------------------------------------------------------------------------------

. est store mod4  

. margins, dydx(profile2) at(trumpft2=(0(0.1)1))  

Average marginal effects                        Number of obs     =        815
Model VCE    : OLS

Expression   : Linear prediction, predict()
dy/dx w.r.t. : profile2

1._at        : trumpft2        =           0

2._at        : trumpft2        =          .1

3._at        : trumpft2        =          .2

4._at        : trumpft2        =          .3

5._at        : trumpft2        =          .4

6._at        : trumpft2        =          .5

7._at        : trumpft2        =          .6

8._at        : trumpft2        =          .7

9._at        : trumpft2        =          .8

10._at       : trumpft2        =          .9

11._at       : trumpft2        =           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
profile2     |
         _at |
          1  |   1.236705   .2189175     5.65   0.000     .8069824    1.666427
          2  |   1.358175   .1926993     7.05   0.000     .9799178    1.736433
          3  |   1.479646   .1731079     8.55   0.000     1.139845    1.819446
          4  |   1.601116   .1625573     9.85   0.000     1.282026    1.920207
          5  |   1.722587   .1628146    10.58   0.000     1.402991    2.042182
          6  |   1.844057   .1738319    10.61   0.000     1.502835    2.185279
          7  |   1.965528   .1937825    10.14   0.000     1.585144    2.345911
          8  |   2.086998   .2202521     9.48   0.000     1.654656     2.51934
          9  |   2.208469   .2511882     8.79   0.000     1.715401    2.701536
         10  |   2.329939   .2851407     8.17   0.000     1.770225    2.889653
         11  |    2.45141   .3211544     7.63   0.000     1.821002    3.081817
------------------------------------------------------------------------------

. matrix list r(table)

r(table)[9,11]
         profile2:  profile2:  profile2:  profile2:  profile2:  profile2:  profile2:  profile2:  profile2:  profile2:  profile2:
                1.         2.         3.         4.         5.         6.         7.         8.         9.        10.        11.
              _at        _at        _at        _at        _at        _at        _at        _at        _at        _at        _at
     b  1.2367047  1.3581752  1.4796457  1.6011161  1.7225866  1.8440571  1.9655276  2.0869981  2.2084686   2.329939  2.4514095
    se  .21891754  .19269929   .1731079  .16255728  .16281461  .17383188  .19378247  .22025205  .25118816  .28514068  .32115438
     t  5.6491804  7.0481585   8.547534  9.8495502   10.58005  10.608279  10.142959  9.4754988  8.7920886  8.1711912  7.6331189
pvalue  2.243e-08  3.925e-12  6.356e-17  1.117e-21  1.431e-24  1.098e-24  8.035e-23  2.938e-20  8.928e-18  1.194e-15  6.544e-14
    ll  .80698242  .97991778   1.139845  1.2820258  1.4029911  1.5028354   1.585144  1.6546562   1.715401  1.7702246  1.8210024
    ul  1.6664269  1.7364325  1.8194463  1.9202065  2.0421821  2.1852789  2.3459112  2.5193399  2.7015362  2.8896534  3.0818167
    df        798        798        798        798        798        798        798        798        798        798        798
  crit  1.9629412  1.9629412  1.9629412  1.9629412  1.9629412  1.9629412  1.9629412  1.9629412  1.9629412  1.9629412  1.9629412
 eform          0          0          0          0          0          0          0          0          0          0          0

. matrix m0 = r(at)

. matrix  m11 = m0[1..., "trumpft2"]

. matrix m1 = r(table)

. matrix  m12 = m1["b", 1...]'

. matrix m13 = m1["ll".."ul",1...]'

. matrix first = m11,m12,m13

. 
. matrix rownames first = 1

. matrix colnames first = scale estimate lower upper

. 
. matrix RESULTS = first

. 
. * Save predictions
. preserve

. matsave RESULTS, dropall
data in memory will be dropped
Press any key to continue, or Break to abort
matrix RESULTS saved

. rename _rowname order

. saveold "Justified Marginal Effects.dta", version(12) replace
(saving in Stata 12 format, which can be read by Stata 11 or 12)
file Justified Marginal Effects.dta saved

. erase RESULTS.dta

. clear

. restore 

. 
. * Save model estimates
. esttab mod1 mod2 mod3 mod4 using "Justified Estimates.tex", ///
>         cells(b(star fmt(3)) se(par fmt(3))) legend label ///
>         varlabels(_cons Constant) stats(r2 N, fmt(3 0 1)) ///
>         addnotes(Note: OLS coefficients with standard errors in parentheses)    
(output written to Justified Estimates.tex)

. 
. ********************************************************************************
. 
. ****
. ** Controlling for everything 
. ** separately
. ****
. 
. * Sans profile
. reg justified pid2 ideo2 interest2 college age2 income2 female white south ///
>         egocentric systemic anxiety powerless conthink whiteidentity ///
>         corrupt trustgov raceresent populism authoritarian insured military
note: white omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =       644
-------------+----------------------------------   F(21, 622)      =     19.85
       Model |  457.318737        21  21.7770827   Prob > F        =    0.0000
    Residual |  682.482506       622  1.09723875   R-squared       =    0.4012
-------------+----------------------------------   Adj R-squared   =    0.3810
       Total |  1139.80124       643  1.77263024   Root MSE        =    1.0475

-------------------------------------------------------------------------------
    justified |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
         pid2 |   .2732695   .1527662     1.79   0.074    -.0267304    .5732695
        ideo2 |  -.1043618   .1943819    -0.54   0.592     -.486086    .2773625
    interest2 |   .0865232   .1548471     0.56   0.577    -.2175632    .3906096
      college |   .0999956   .0920704     1.09   0.278     -.080811    .2808022
         age2 |  -1.569311   .2231133    -7.03   0.000    -2.007457   -1.131164
      income2 |   .2218894   .1652614     1.34   0.180    -.1026485    .5464273
       female |  -.0563382   .0922803    -0.61   0.542    -.2375569    .1248804
        white |          0  (omitted)
        south |  -.0157365   .0890173    -0.18   0.860    -.1905474    .1590745
   egocentric |   .1676439   .0581464     2.88   0.004      .053457    .2818309
     systemic |   .0567447   .0627519     0.90   0.366    -.0664866     .179976
      anxiety |  -.0614923   .0627712    -0.98   0.328    -.1847615     .061777
    powerless |  -.0192425   .0629068    -0.31   0.760     -.142778    .1042931
     conthink |   .0878691   .0622726     1.41   0.159     -.034421    .2101591
whiteidentity |   .3008761   .0554698     5.42   0.000     .1919454    .4098068
      corrupt |   .1674054   .0551948     3.03   0.003     .0590147     .275796
     trustgov |   .1301658   .0506085     2.57   0.010     .0307815      .22955
   raceresent |   .0966267   .0524732     1.84   0.066    -.0064194    .1996728
     populism |   .0375988   .0834039     0.45   0.652    -.1261886    .2013862
authoritarian |   .2426895   .0563307     4.31   0.000     .1320682    .3533109
      insured |  -.0832656   .1689118    -0.49   0.622    -.4149721    .2484409
     military |   .2102926   .1214139     1.73   0.084    -.0281383    .4487234
        _cons |  -1.377683   .4198534    -3.28   0.001    -2.202185   -.5531812
-------------------------------------------------------------------------------

. est store mod5  

.         
. * With Trump support    
. reg justified pid2 ideo2 interest2 college age2 income2 female white south ///
>         trumpft2 egocentric systemic anxiety powerless conthink whiteidentity ///
>         corrupt trustgov raceresent populism authoritarian insured military
note: white omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =       644
-------------+----------------------------------   F(22, 621)      =     25.43
       Model |  540.191438        22  24.5541563   Prob > F        =    0.0000
    Residual |  599.609804       621   .96555524   R-squared       =    0.4739
-------------+----------------------------------   Adj R-squared   =    0.4553
       Total |  1139.80124       643  1.77263024   Root MSE        =    .98263

-------------------------------------------------------------------------------
    justified |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
         pid2 |  -.3959344   .1604819    -2.47   0.014    -.7110873   -.0807815
        ideo2 |  -.4032734   .1851774    -2.18   0.030    -.7669233   -.0396236
    interest2 |  -.0193942   .1457076    -0.13   0.894    -.3055335     .266745
      college |   .1123331   .0863793     1.30   0.194    -.0572979    .2819641
         age2 |  -1.371848   .2103797    -6.52   0.000     -1.78499   -.9587066
      income2 |   .1692965   .1551317     1.09   0.276    -.1353497    .4739427
       female |  -.0679626    .086575    -0.79   0.433    -.2379779    .1020526
        white |          0  (omitted)
        south |  -.0434003   .0835584    -0.52   0.604    -.2074916     .120691
     trumpft2 |   1.420029    .153278     9.26   0.000     1.119022    1.721035
   egocentric |   .1703058   .0545465     3.12   0.002     .0631879    .2774237
     systemic |   .0479314   .0588737     0.81   0.416    -.0676843    .1635472
      anxiety |  -.0549202   .0588885    -0.93   0.351    -.1705648    .0607245
    powerless |   .0170072    .059141     0.29   0.774    -.0991334    .1331477
     conthink |    .052816   .0585389     0.90   0.367    -.0621421    .1677742
whiteidentity |   .2428965   .0524099     4.63   0.000     .1399745    .3458186
      corrupt |   .1316782   .0519203     2.54   0.011     .0297175    .2336388
     trustgov |   .1157566   .0475001     2.44   0.015     .0224763    .2090369
   raceresent |    .012535   .0500537     0.25   0.802    -.0857601    .1108301
     populism |   .0218233   .0782578     0.28   0.780    -.1318586    .1755052
authoritarian |   .2476942   .0528452     4.69   0.000     .1439173    .3514712
      insured |  -.0673115   .1584615    -0.42   0.671    -.3784967    .2438738
     military |   .1113306   .1143953     0.97   0.331    -.1133178    .3359791
        _cons |  -.7496381    .399646    -1.88   0.061    -1.534459    .0351832
-------------------------------------------------------------------------------

. est store mod6  

. 
. * With profile  
. reg justified pid2 ideo2 interest2 college age2 income2 female white south ///
>         trumpft2 egocentric systemic anxiety powerless conthink whiteidentity ///
>         corrupt trustgov raceresent populism authoritarian insured military ///
>         profile2 
note: white omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =       644
-------------+----------------------------------   F(23, 620)      =     25.25
       Model |  551.231991        23  23.9666083   Prob > F        =    0.0000
    Residual |  588.569251       620  .949305243   R-squared       =    0.4836
-------------+----------------------------------   Adj R-squared   =    0.4645
       Total |  1139.80124       643  1.77263024   Root MSE        =    .97432

-------------------------------------------------------------------------------
    justified |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
         pid2 |  -.4152207   .1592262    -2.61   0.009    -.7279086   -.1025327
        ideo2 |  -.3625689   .1840001    -1.97   0.049    -.7239079   -.0012299
    interest2 |  -.0318313   .1445223    -0.22   0.826    -.3156438    .2519811
      college |   .0928928   .0858389     1.08   0.280    -.0756773     .261463
         age2 |  -1.305172   .2095161    -6.23   0.000    -1.716619   -.8937249
      income2 |   .1467014   .1539633     0.95   0.341    -.1556514    .4490543
       female |  -.0824479   .0859484    -0.96   0.338    -.2512332    .0863374
        white |          0  (omitted)
        south |  -.0463166   .0828567    -0.56   0.576    -.2090304    .1163972
     trumpft2 |   1.422471   .1519844     9.36   0.000     1.124004    1.720937
   egocentric |   .1575385   .0542149     2.91   0.004     .0510713    .2640056
     systemic |  -.0763617   .0688195    -1.11   0.268    -.2115093    .0587858
      anxiety |   -.065997   .0584811    -1.13   0.260     -.180842     .048848
    powerless |   .0144119   .0586461     0.25   0.806    -.1007572    .1295811
     conthink |   .0519371   .0580447     0.89   0.371     -.062051    .1659252
whiteidentity |   .2300557   .0521032     4.42   0.000     .1277356    .3323759
      corrupt |   .1158048   .0516915     2.24   0.025     .0142931    .2173165
     trustgov |   .1037894   .0472292     2.20   0.028     .0110407    .1965381
   raceresent |  -.0055504   .0499133    -0.11   0.911      -.10357    .0924692
     populism |  -.0747071   .0825979    -0.90   0.366    -.2369126    .0874984
authoritarian |   .1989743   .0543112     3.66   0.000     .0923181    .3056306
      insured |  -.0637178   .1571259    -0.41   0.685    -.3722813    .2448457
     military |    .044496   .1151092     0.39   0.699    -.1815551    .2705471
     profile2 |   1.181454   .3464371     3.41   0.001     .5011219    1.861787
        _cons |  -.1122974   .4381275    -0.26   0.798     -.972691    .7480963
-------------------------------------------------------------------------------

. est store mod7  

. 
. margins, at(trumpft2=(0(0.1)1)) 

Predictive margins                              Number of obs     =        644
Model VCE    : OLS

Expression   : Linear prediction, predict()

1._at        : trumpft2        =           0

2._at        : trumpft2        =          .1

3._at        : trumpft2        =          .2

4._at        : trumpft2        =          .3

5._at        : trumpft2        =          .4

6._at        : trumpft2        =          .5

7._at        : trumpft2        =          .6

8._at        : trumpft2        =          .7

9._at        : trumpft2        =          .8

10._at       : trumpft2        =          .9

11._at       : trumpft2        =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   1.349935   .0719324    18.77   0.000     1.208674    1.491196
          2  |   1.492182   .0596342    25.02   0.000     1.375073    1.609292
          3  |   1.634429   .0489918    33.36   0.000     1.538219    1.730639
          4  |   1.776676   .0413055    43.01   0.000     1.695561    1.857792
          5  |   1.918923   .0383937    49.98   0.000     1.843526    1.994321
          6  |    2.06117   .0412795    49.93   0.000     1.980106    2.142235
          7  |   2.203418   .0489479    45.02   0.000     2.107294    2.299541
          8  |   2.345665   .0595801    39.37   0.000     2.228661    2.462668
          9  |   2.487912   .0718725    34.62   0.000     2.346769    2.629055
         10  |   2.630159   .0851088    30.90   0.000     2.463022    2.797295
         11  |   2.772406   .0989109    28.03   0.000     2.578165    2.966647
------------------------------------------------------------------------------

. matrix list r(table)

r(table)[9,11]
                1.         2.         3.         4.         5.         6.         7.         8.         9.        10.        11.
              _at        _at        _at        _at        _at        _at        _at        _at        _at        _at        _at
     b  1.3499351  1.4921821  1.6344292  1.7766763  1.9189233  2.0611704  2.2034175  2.3456645  2.4879116  2.6301586  2.7724057
    se  .07193236  .05963422  .04899184  .04130552  .03839372  .04127947   .0489479  .05958006   .0718725  .08510883  .09891085
     t  18.766729  25.022248  33.361254  43.013042  49.980136    49.9321  45.015572  39.369961  34.615627  30.903475  28.029338
pvalue  1.457e-62  4.709e-96  1.65e-140  2.93e-188  1.24e-219  2.00e-219  1.52e-197  8.27e-171  5.56e-147  1.25e-127  2.70e-112
    ll  1.2086745  1.3750726  1.5382191  1.6955606  1.8435259  1.9801059  2.1072938  2.2286614  2.3467686  2.4630221  2.5781649
    ul  1.4911957  1.6092917  1.7306393   1.857792  1.9943208  2.1422349  2.2995413  2.4626677  2.6290547  2.7972952  2.9666466
    df        620        620        620        620        620        620        620        620        620        620        620
  crit  1.9637976  1.9637976  1.9637976  1.9637976  1.9637976  1.9637976  1.9637976  1.9637976  1.9637976  1.9637976  1.9637976
 eform          0          0          0          0          0          0          0          0          0          0          0

. matrix m0 = r(at)

. matrix  m11 = m0[1..., "trumpft2"]

. matrix m1 = r(table)

. matrix  m12 = m1["b", 1...]'

. matrix m13 = m1["ll".."ul",1...]'

. matrix first = m11,m12,m13

. 
. margins, at(profile2=(0(0.1)1)) 

Predictive margins                              Number of obs     =        644
Model VCE    : OLS

Expression   : Linear prediction, predict()

1._at        : profile2        =           0

2._at        : profile2        =          .1

3._at        : profile2        =          .2

4._at        : profile2        =          .3

5._at        : profile2        =          .4

6._at        : profile2        =          .5

7._at        : profile2        =          .6

8._at        : profile2        =          .7

9._at        : profile2        =          .8

10._at       : profile2        =          .9

11._at       : profile2        =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   1.526201   .1214816    12.56   0.000     1.287636    1.764766
          2  |   1.644346   .0892874    18.42   0.000     1.469004    1.819689
          3  |   1.762492   .0598923    29.43   0.000     1.644876    1.880108
          4  |   1.880637   .0400288    46.98   0.000     1.802029    1.959246
          5  |   1.998783    .044921    44.50   0.000     1.910567    2.086998
          6  |   2.116928    .069526    30.45   0.000     1.980393    2.253463
          7  |   2.235074   .1002507    22.29   0.000     2.038201    2.431946
          8  |   2.353219    .132917    17.70   0.000     2.092197    2.614241
          9  |   2.471364   .1663851    14.85   0.000     2.144618    2.798111
         10  |    2.58951   .2002535    12.93   0.000     2.196252    2.982767
         11  |   2.707655   .2343487    11.55   0.000     2.247442    3.167869
------------------------------------------------------------------------------

. matrix m2 = r(at)

. matrix  m21 = m2[1..., "profile2"]

. matrix m3 = r(table)

. matrix m31 = m3["b", 1...]'

. matrix m32 = m3["ll".."ul",1...]'

. matrix second = m21,m31,m32

. 
. matrix rownames first = 1

. matrix rownames second = 2

. matrix colnames first = scale estimate lower upper

. 
. matrix RESULTS = first \ second

. 
. * Save predictions
. preserve

. matsave RESULTS, dropall
data in memory will be dropped
Press any key to continue, or Break to abort
matrix RESULTS saved

. rename _rowname order

. saveold "Justified Predictions, NBREG.dta", version(12) replace
(saving in Stata 12 format, which can be read by Stata 11 or 12)
file Justified Predictions, NBREG.dta saved

. erase RESULTS.dta

. clear

. restore 

.                 
. * With profile and Trump thermometer interactions               
. reg justified pid2 ideo2 interest2 college age2 income2 female white south ///
>         egocentric systemic anxiety powerless conthink whiteidentity ///
>         corrupt trustgov raceresent populism authoritarian insured military ///
>         c.trumpft2##c.profile2          
note: white omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =       644
-------------+----------------------------------   F(24, 619)      =     24.48
       Model |  555.053335        24  23.1272223   Prob > F        =    0.0000
    Residual |  584.747908       619   .94466544   R-squared       =    0.4870
-------------+----------------------------------   Adj R-squared   =    0.4671
       Total |  1139.80124       643  1.77263024   Root MSE        =    .97194

---------------------------------------------------------------------------------------
            justified |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                 pid2 |  -.3960777   .1591215    -2.49   0.013     -.708561   -.0835943
                ideo2 |  -.3119482   .1852675    -1.68   0.093    -.6757772    .0518807
            interest2 |  -.0333837   .1441707    -0.23   0.817    -.3165067    .2497393
              college |   .0842831   .0857358     0.98   0.326    -.0840852    .2526513
                 age2 |  -1.378927   .2121962    -6.50   0.000    -1.795639   -.9622156
              income2 |   .1368323    .153665     0.89   0.374    -.1649356    .4386002
               female |  -.0961018   .0860065    -1.12   0.264    -.2650016     .072798
                white |          0  (omitted)
                south |  -.0429909   .0826705    -0.52   0.603    -.2053396    .1193577
           egocentric |    .154139   .0541087     2.85   0.005     .0478801    .2603978
             systemic |  -.0869868   .0688541    -1.26   0.207    -.2222027    .0482291
              anxiety |  -.0780807   .0586466    -1.33   0.184    -.1932511    .0370896
            powerless |   .0299608   .0590112     0.51   0.612    -.0859257    .1458473
             conthink |   .0583664   .0579909     1.01   0.315    -.0555163    .1722492
        whiteidentity |   .2314506   .0519803     4.45   0.000     .1293714    .3335298
              corrupt |   .1156044   .0515651     2.24   0.025     .0143406    .2168682
             trustgov |   .0990859   .0471717     2.10   0.036       .00645    .1917218
           raceresent |   .0101537   .0503997     0.20   0.840    -.0888213    .1091287
             populism |  -.0705251    .082422    -0.86   0.393    -.2323858    .0913356
        authoritarian |   .1958464   .0542007     3.61   0.000      .089407    .3022859
              insured |  -.0631275   .1567417    -0.40   0.687    -.3709375    .2446825
             military |   .0282047   .1151129     0.25   0.807    -.1978543    .2542638
             trumpft2 |   1.033141   .2458812     4.20   0.000     .5502785    1.516003
             profile2 |   .8322711   .3867478     2.15   0.032     .0727743    1.591768
                      |
c.trumpft2#c.profile2 |   .9683479   .4814624     2.01   0.045     .0228502    1.913846
                      |
                _cons |  -.0142581   .4397653    -0.03   0.974     -.877871    .8493547
---------------------------------------------------------------------------------------

. est store mod8

. margins, dydx(profile2) at(trumpft2=(0(0.1)1))  

Average marginal effects                        Number of obs     =        644
Model VCE    : OLS

Expression   : Linear prediction, predict()
dy/dx w.r.t. : profile2

1._at        : trumpft2        =           0

2._at        : trumpft2        =          .1

3._at        : trumpft2        =          .2

4._at        : trumpft2        =          .3

5._at        : trumpft2        =          .4

6._at        : trumpft2        =          .5

7._at        : trumpft2        =          .6

8._at        : trumpft2        =          .7

9._at        : trumpft2        =          .8

10._at       : trumpft2        =          .9

11._at       : trumpft2        =           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
profile2     |
         _at |
          1  |   .8322711   .3867478     2.15   0.032     .0727743    1.591768
          2  |   .9291058   .3676604     2.53   0.012     .2070929    1.651119
          3  |   1.025941   .3541337     2.90   0.004     .3304915     1.72139
          4  |   1.122775   .3468188     3.24   0.001     .4416914    1.803859
          5  |    1.21961   .3461098     3.52   0.000     .5399185    1.899302
          6  |   1.316445   .3520466     3.74   0.000     .6250945    2.007796
          7  |    1.41328   .3643045     3.88   0.000     .6978572    2.128702
          8  |   1.510115   .3822759     3.95   0.000     .7593997    2.260829
          9  |   1.606949   .4052013     3.97   0.000     .8112136    2.402685
         10  |   1.703784   .4322932     3.94   0.000     .8548452    2.552723
         11  |   1.800619   .4628205     3.89   0.000     .8917302    2.709508
------------------------------------------------------------------------------

. matrix list r(table)

r(table)[9,11]
         profile2:  profile2:  profile2:  profile2:  profile2:  profile2:  profile2:  profile2:  profile2:  profile2:  profile2:
                1.         2.         3.         4.         5.         6.         7.         8.         9.        10.        11.
              _at        _at        _at        _at        _at        _at        _at        _at        _at        _at        _at
     b  .83227105  .92910584  1.0259406  1.1227754  1.2196102   1.316445  1.4132798  1.5101146  1.6069494  1.7037841  1.8006189
    se  .38674779  .36766045   .3541337  .34681879   .3461098  .35204663  .36430453  .38227591  .40520128  .43229318  .46282053
     t  2.1519736  2.5270758  2.8970432  3.2373546  3.5237668  3.7394052  3.8793912  3.9503263  3.9658053    3.94127  3.8905339
pvalue  .03178547  .01174959  .00390011  .00127088  .00045683  .00020157  .00011594  .00008703   .0000817  .00009029  .00011086
    ll  .07277427  .20709286  .33049152  .44169137  .53991848  .62509449  .69785719  .75939969  .81121357  .85484515  .89173024
    ul  1.5917678  1.6511188  1.7213897  1.8038595  1.8993019  2.0077955  2.1287024  2.2608294  2.4026852  2.5527231  2.7095077
    df        619        619        619        619        619        619        619        619        619        619        619
  crit  1.9638038  1.9638038  1.9638038  1.9638038  1.9638038  1.9638038  1.9638038  1.9638038  1.9638038  1.9638038  1.9638038
 eform          0          0          0          0          0          0          0          0          0          0          0

. matrix m0 = r(at)

. matrix  m11 = m0[1..., "trumpft2"]

. matrix m1 = r(table)

. matrix  m12 = m1["b", 1...]'

. matrix m13 = m1["ll".."ul",1...]'

. matrix first = m11,m12,m13

. 
. matrix rownames first = 1

. matrix colnames first = scale estimate lower upper

. 
. matrix RESULTS = first

. 
. * Save predictions
. preserve

. matsave RESULTS, dropall
data in memory will be dropped
Press any key to continue, or Break to abort
matrix RESULTS saved

. rename _rowname order

. saveold "Justified Marginal Effects, NBREG.dta", version(12) replace
(saving in Stata 12 format, which can be read by Stata 11 or 12)
file Justified Marginal Effects, NBREG.dta saved

. erase RESULTS.dta

. clear

. restore 

. 
. * Save model estimates
. esttab mod5 mod6 mod7 mod8 using "Garbage Can Estimates.tex", ///
>         cells(b(star fmt(3)) se(par fmt(3))) legend label ///
>         varlabels(_cons Constant) stats(r2 N, fmt(3 0 1)) ///
>         addnotes(Note: OLS coefficients with standard errors in parentheses.)   
(output written to Garbage Can Estimates.tex)

.         
. ********************************************************************************
. 
. ****
. ** Using negative binomial
. ****
. 
. * Naive model
. nbreg justified i.pid3 i.ideo3 pidstrength2 ideostrength2 interest2 edu2 ///
>         age2 income2 female white south

Fitting Poisson model:

Iteration 0:   log likelihood =  -1228.194  
Iteration 1:   log likelihood =  -1228.194  

Fitting constant-only model:

Iteration 0:   log likelihood = -1522.7445  
Iteration 1:   log likelihood = -1298.4553  
Iteration 2:   log likelihood = -1298.4553  (backed up)

Fitting full model:

Iteration 0:   log likelihood = -1229.8608  
Iteration 1:   log likelihood = -1228.1949  
Iteration 2:   log likelihood =  -1228.194  
Iteration 3:   log likelihood =  -1228.194  

Negative binomial regression                    Number of obs     =        815
                                                LR chi2(13)       =     140.52
Dispersion     = mean                           Prob > chi2       =     0.0000
Log likelihood =  -1228.194                     Pseudo R2         =     0.0541

-------------------------------------------------------------------------------
    justified |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
         pid3 |
           2  |   .3758455   .1316143     2.86   0.004     .1178861    .6338048
           3  |   .1551046   .0730829     2.12   0.034     .0118648    .2983445
              |
        ideo3 |
           2  |   .2531614   .1121003     2.26   0.024     .0334489     .472874
           3  |   .1192366   .0779843     1.53   0.126    -.0336097     .272083
              |
 pidstrength2 |   .4128785   .1230149     3.36   0.001     .1717737    .6539833
ideostrength2 |   .3566364   .1292608     2.76   0.006     .1032899    .6099828
    interest2 |  -.0909672   .0960601    -0.95   0.344    -.2792415    .0973071
         edu2 |   .3315607   .1101158     3.01   0.003     .1157376    .5473837
         age2 |  -1.102144   .1232538    -8.94   0.000    -1.343717   -.8605708
      income2 |  -.0799472   .0948577    -0.84   0.399    -.2658649    .1059706
       female |  -.1811046   .0528134    -3.43   0.001    -.2846168   -.0775923
        white |   .0302323    .073654     0.41   0.681     -.114127    .1745915
        south |   .0087181   .0531851     0.16   0.870    -.0955228    .1129589
        _cons |   .3284738   .1613838     2.04   0.042     .0121673    .6447803
--------------+----------------------------------------------------------------
     /lnalpha |  -43.78112          .                             .           .
--------------+----------------------------------------------------------------
        alpha |   9.69e-20          .                             .           .
-------------------------------------------------------------------------------
LR test of alpha=0: chibar2(01) = 0.00                 Prob >= chibar2 = 1.000

. est store mod9

. 
. * With Trump thermometer
. nbreg justified i.pid3 i.ideo3 pidstrength2 ideostrength2 interest2 edu2 ///
>         age2 income2 female white south trumpft2

Fitting Poisson model:

Iteration 0:   log likelihood = -1192.3711  
Iteration 1:   log likelihood = -1192.3711  

Fitting constant-only model:

Iteration 0:   log likelihood = -1522.7445  
Iteration 1:   log likelihood = -1298.4553  
Iteration 2:   log likelihood = -1298.4553  (backed up)

Fitting full model:

Iteration 0:   log likelihood = -1196.0853  
Iteration 1:   log likelihood = -1192.3752  
Iteration 2:   log likelihood = -1192.3711  
Iteration 3:   log likelihood = -1192.3711  

Negative binomial regression                    Number of obs     =        815
                                                LR chi2(14)       =     212.17
Dispersion     = mean                           Prob > chi2       =     0.0000
Log likelihood = -1192.3711                     Pseudo R2         =     0.0817

-------------------------------------------------------------------------------
    justified |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
         pid3 |
           2  |   .1206828   .1354146     0.89   0.373     -.144725    .3860905
           3  |  -.2126681   .0862946    -2.46   0.014    -.3818025   -.0435337
              |
        ideo3 |
           2  |   .1136711   .1124198     1.01   0.312    -.1066677    .3340099
           3  |  -.0430867   .0812293    -0.53   0.596    -.2022932    .1161198
              |
 pidstrength2 |   .2711986   .1234876     2.20   0.028     .0291674    .5132299
ideostrength2 |   .2062534   .1307001     1.58   0.115    -.0499141    .4624208
    interest2 |  -.1145184   .0961693    -1.19   0.234    -.3030066    .0739699
         edu2 |   .3119975   .1101078     2.83   0.005     .0961901    .5278049
         age2 |  -1.008149   .1252326    -8.05   0.000      -1.2536   -.7626976
      income2 |  -.1145281   .0950638    -1.20   0.228    -.3008496    .0717934
       female |  -.1614182   .0533586    -3.03   0.002    -.2659992   -.0568371
        white |   .0080761   .0737724     0.11   0.913    -.1365151    .1526673
        south |  -.0135603   .0532884    -0.25   0.799    -.1180037    .0908831
     trumpft2 |   .8078407   .0951006     8.49   0.000      .621447    .9942344
        _cons |    .474119   .1605125     2.95   0.003     .1595202    .7887178
--------------+----------------------------------------------------------------
     /lnalpha |  -43.78112          .                             .           .
--------------+----------------------------------------------------------------
        alpha |   9.69e-20          .                             .           .
-------------------------------------------------------------------------------
LR test of alpha=0: chibar2(01) = 0.00                 Prob >= chibar2 = 1.000

. est store mod10 

. 
. * With Trump thermometer + violence profile
. nbreg justified i.pid3 i.ideo3 pidstrength2 ideostrength2 interest2 edu2 ///
>         age2 income2 female white south trumpft2 profile2

Fitting Poisson model:

Iteration 0:   log likelihood = -1166.6321  
Iteration 1:   log likelihood = -1166.6268  
Iteration 2:   log likelihood = -1166.6268  

Fitting constant-only model:

Iteration 0:   log likelihood = -1522.7445  
Iteration 1:   log likelihood = -1298.4553  
Iteration 2:   log likelihood = -1298.4553  (backed up)

Fitting full model:

Iteration 0:   log likelihood = -1172.1381  
Iteration 1:   log likelihood = -1166.6356  
Iteration 2:   log likelihood = -1166.6268  
Iteration 3:   log likelihood = -1166.6268  

Negative binomial regression                    Number of obs     =        815
                                                LR chi2(15)       =     263.66
Dispersion     = mean                           Prob > chi2       =     0.0000
Log likelihood = -1166.6268                     Pseudo R2         =     0.1015

-------------------------------------------------------------------------------
    justified |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
         pid3 |
           2  |   .1295506   .1345545     0.96   0.336    -.1341714    .3932727
           3  |   -.176461   .0841169    -2.10   0.036    -.3413271    -.011595
              |
        ideo3 |
           2  |    .116252   .1120467     1.04   0.299    -.1033555    .3358595
           3  |   .0210534   .0797378     0.26   0.792    -.1352297    .1773365
              |
 pidstrength2 |   .2721468   .1235144     2.20   0.028     .0300631    .5142305
ideostrength2 |   .1279592    .130989     0.98   0.329    -.1287745    .3846928
    interest2 |  -.1503083   .0957888    -1.57   0.117    -.3380508    .0374343
         edu2 |   .2375049   .1108186     2.14   0.032     .0203044    .4547054
         age2 |  -.8034593   .1302781    -6.17   0.000      -1.0588   -.5481188
      income2 |  -.0685386   .0954579    -0.72   0.473    -.2556326    .1185554
       female |  -.1181084   .0540424    -2.19   0.029    -.2240296   -.0121873
        white |   .0586003   .0746549     0.78   0.432    -.0877207    .2049213
        south |  -.0151939   .0533485    -0.28   0.776     -.119755    .0893672
     trumpft2 |   .6924801   .0944066     7.34   0.000     .5074464    .8775137
     profile2 |   .7423739   .1017522     7.30   0.000     .5429433    .9418046
        _cons |   .1406336   .1668966     0.84   0.399    -.1864778     .467745
--------------+----------------------------------------------------------------
     /lnalpha |  -43.78112          .                             .           .
--------------+----------------------------------------------------------------
        alpha |   9.69e-20          .                             .           .
-------------------------------------------------------------------------------
LR test of alpha=0: chibar2(01) = 0.00                 Prob >= chibar2 = 1.000

. est store mod11 

. 
. * With interaction between Trump thermometer and violence profile
. nbreg justified i.pid3 i.ideo3 pidstrength2 ideostrength2 interest2 edu2 ///
>         age2 income2 female white south c.trumpft2##c.profile2

Fitting Poisson model:

Iteration 0:   log likelihood = -1166.6321  
Iteration 1:   log likelihood = -1166.5263  
Iteration 2:   log likelihood = -1166.5263  

Fitting constant-only model:

Iteration 0:   log likelihood = -1522.7445  
Iteration 1:   log likelihood = -1298.4553  
Iteration 2:   log likelihood = -1298.4553  (backed up)

Fitting full model:

Iteration 0:   log likelihood = -1172.1359  
Iteration 1:   log likelihood = -1166.5354  
Iteration 2:   log likelihood = -1166.5263  
Iteration 3:   log likelihood = -1166.5263  

Negative binomial regression                    Number of obs     =        815
                                                LR chi2(16)       =     263.86
Dispersion     = mean                           Prob > chi2       =     0.0000
Log likelihood = -1166.5263                     Pseudo R2         =     0.1016

---------------------------------------------------------------------------------------
            justified |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                 pid3 |
                   2  |   .1234762   .1352829     0.91   0.361    -.1416735    .3886259
                   3  |  -.1797792   .0845082    -2.13   0.033    -.3454123    -.014146
                      |
                ideo3 |
                   2  |   .1144907   .1120979     1.02   0.307    -.1052172    .3341986
                   3  |   .0145035   .0811303     0.18   0.858    -.1445089    .1735159
                      |
         pidstrength2 |   .2683361   .1238141     2.17   0.030      .025665    .5110073
        ideostrength2 |   .1280391   .1309437     0.98   0.328    -.1286058     .384684
            interest2 |  -.1478887   .0959241    -1.54   0.123    -.3358964    .0401191
                 edu2 |   .2423359   .1113442     2.18   0.030     .0241053    .4605666
                 age2 |  -.7976818   .1308621    -6.10   0.000    -1.054167   -.5411967
              income2 |  -.0672487   .0955261    -0.70   0.481    -.2544764     .119979
               female |  -.1168255   .0541389    -2.16   0.031    -.2229358   -.0107152
                white |   .0607093   .0748537     0.81   0.417    -.0860014    .2074199
                south |  -.0152356   .0533481    -0.29   0.775    -.1197959    .0893247
             trumpft2 |   .7451326   .1507336     4.94   0.000     .4497001    1.040565
             profile2 |   .7917693   .1497926     5.29   0.000     .4981812    1.085357
                      |
c.trumpft2#c.profile2 |  -.1138214   .2541091    -0.45   0.654     -.611866    .3842232
                      |
                _cons |   .1164108   .1754451     0.66   0.507    -.2274552    .4602767
----------------------+----------------------------------------------------------------
             /lnalpha |  -43.78112          .                             .           .
----------------------+----------------------------------------------------------------
                alpha |   9.69e-20          .                             .           .
---------------------------------------------------------------------------------------
LR test of alpha=0: chibar2(01) = 0.00                 Prob >= chibar2 = 1.000

. est store mod12 

. 
. * Save model estimates
. esttab mod9 mod10 mod11 mod12 using "NBREG Estimates.tex", ///
>         cells(b(star fmt(3)) se(par fmt(3))) legend label ///
>         varlabels(_cons Constant) stats(r2 N, fmt(3 0 1)) ///
>         addnotes(Note: MLE coefficients with standard errors in parentheses.)   
(output written to NBREG Estimates.tex)

. 
. 
. ********************************************************************************
.         
. **** 
. ** Appendix analyses
. ****
. 
. **** 
. ** Satisficing?
. ****
. 
. * Lower for "satisficers"       
. gen violence_sat = .
(1,002 missing values generated)

. replace violence_sat = 0 if violence_sd == 0
(584 real changes made)

. replace violence_sat = 1 if violence_sd > 0
(418 real changes made)

. ttest violence, by(violence_sat)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     584    1.546233    .0434491    1.049995    1.460897    1.631569
       1 |     418    2.429027    .0466745    .9542632     2.33728    2.520774
---------+--------------------------------------------------------------------
combined |   1,002    1.914504    .0347666    1.100514    1.846281    1.982728
---------+--------------------------------------------------------------------
    diff |           -.8827942    .0647838               -1.009922   -.7556664
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t = -13.6268
Ho: diff = 0                                     degrees of freedom =     1000

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. 
. * No difference
. gen populism_sat = .
(1,002 missing values generated)

. replace populism_sat = 0 if populism_sd == 0
(75 real changes made)

. replace populism_sat = 1 if populism_sd > 0
(927 real changes made)

. ttest populism, by(populism_sat)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |      75        3.44    .1304738    1.129937    3.180025    3.699975
       1 |     927    3.321467    .0249981    .7611103    3.272408    3.370527
---------+--------------------------------------------------------------------
combined |   1,002    3.330339    .0250995    .7945094    3.281086    3.379593
---------+--------------------------------------------------------------------
    diff |            .1185329    .0953552               -.0685862     .305652
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.2431
Ho: diff = 0                                     degrees of freedom =     1000

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.8929         Pr(|T| > |t|) = 0.2141          Pr(T > t) = 0.1071

. 
. * No difference
. gen authoritarian_sat = .
(1,002 missing values generated)

. replace authoritarian_sat = 0 if authoritarian_sd == 0
(215 real changes made)

. replace authoritarian_sat = 1 if authoritarian_sd > 0
(787 real changes made)

. ttest authoritarian, by(authoritarian_sat)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     215    2.934884    .0759048    1.112983    2.785267    3.084501
       1 |     787    2.903431    .0295336    .8285227    2.845457    2.961405
---------+--------------------------------------------------------------------
combined |   1,002     2.91018    .0283266    .8966611    2.854593    2.965766
---------+--------------------------------------------------------------------
    diff |             .031453    .0690284               -.1040041      .16691
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.4557
Ho: diff = 0                                     degrees of freedom =     1000

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6756         Pr(|T| > |t|) = 0.6487          Pr(T > t) = 0.3244

.         
. * Higher for "satisficers"              
. gen whiteidentity_sat = .
(1,002 missing values generated)

. replace whiteidentity_sat = 0 if whiteidentity_sd == 0
(196 real changes made)

. replace whiteidentity_sat = 1 if whiteidentity_sd > 0
(806 real changes made)

. ttest whiteidentity, by(whiteidentity_sat)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     196    3.617347    .0799498    1.119297     3.45967    3.775024
       1 |     621    3.176731    .0345525    .8610442    3.108877    3.244585
---------+--------------------------------------------------------------------
combined |     817    3.282436    .0331561    .9477078    3.217354    3.347517
---------+--------------------------------------------------------------------
    diff |            .4406159    .0761438                .2911548     .590077
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   5.7866
Ho: diff = 0                                     degrees of freedom =      815

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. 
. * No difference
. gen raceresent_sat = .
(1,002 missing values generated)

. replace raceresent_sat = 0 if raceresent_sd == 0
(244 real changes made)

. replace raceresent_sat = 1 if raceresent_sd > 0
(758 real changes made)

. ttest raceresent, by(raceresent_sat)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     244    2.905738    .0970829    1.516483    2.714506    3.096969
       1 |     758    2.956794    .0307947    .8478331    2.896341    3.017247
---------+--------------------------------------------------------------------
combined |   1,002    2.944361    .0331686    1.049933    2.879273    3.009449
---------+--------------------------------------------------------------------
    diff |           -.0510565    .0773015               -.2027483    .1006353
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.6605
Ho: diff = 0                                     degrees of freedom =     1000

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.2545         Pr(|T| > |t|) = 0.5091          Pr(T > t) = 0.7455

.         
. * Lower for "satisficers"               
. gen systemic_sat = .
(1,002 missing values generated)

. replace systemic_sat = 0 if systemic_sd == 0
(206 real changes made)

. replace systemic_sat = 1 if systemic_sd > 0
(796 real changes made)

. ttest systemic, by(systemic_sat)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     206    2.291262    .0969001    1.390778    2.100214    2.482311
       1 |     796    2.741206    .0319056    .9001666    2.678577    2.803835
---------+--------------------------------------------------------------------
combined |   1,002    2.648703    .0327205    1.035746    2.584494    2.712911
---------+--------------------------------------------------------------------
    diff |           -.4499439     .079746               -.6064327   -.2934551
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -5.6422
Ho: diff = 0                                     degrees of freedom =     1000

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. 
. * Lower for "satisficers"               
. gen egocentric_sat = .
(1,002 missing values generated)

. replace egocentric_sat = 0 if egocentric_sd == 0
(320 real changes made)

. replace egocentric_sat = 1 if egocentric_sd > 0
(682 real changes made)

. ttest egocentric, by(egocentric_sat)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     320     2.46875    .0739137    1.322209     2.32333     2.61417
       1 |     682    2.800953    .0357374    .9332863    2.730784    2.871122
---------+--------------------------------------------------------------------
combined |   1,002     2.69486    .0342253    1.083382    2.627699    2.762022
---------+--------------------------------------------------------------------
    diff |           -.3322031    .0726904               -.4748462   -.1895599
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -4.5701
Ho: diff = 0                                     degrees of freedom =     1000

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. 
. * Lower for "satisficers"               
. gen anxiety_sat = .
(1,002 missing values generated)

. replace anxiety_sat = 0 if anxiety_sd == 0
(320 real changes made)

. replace anxiety_sat = 1 if anxiety_sd > 0
(682 real changes made)

. ttest anxiety, by(anxiety_sat)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     320     1.39375    .0466751    .8349488     1.30192     1.48558
       1 |     682    2.044624    .0281147    .7342195    1.989422    2.099826
---------+--------------------------------------------------------------------
combined |   1,002     1.83676    .0260716    .8252809    1.785599    1.887921
---------+--------------------------------------------------------------------
    diff |           -.6508737    .0520246               -.7529635   -.5487838
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t = -12.5109
Ho: diff = 0                                     degrees of freedom =     1000

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. 
. * Lower for "satisficers" 
. gen powerless_sat = .
(1,002 missing values generated)

. replace powerless_sat = 0 if powerless_sd == 0
(614 real changes made)

. replace powerless_sat = 1 if powerless_sd > 0
(388 real changes made)

. ttest powerless, by(powerless_sat)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     614    2.239414    .0338712    .8392941    2.172896    2.305931
       1 |     386    2.392487    .0358304    .7039558    2.322039    2.462935
---------+--------------------------------------------------------------------
combined |   1,000      2.2985    .0250754    .7929543    2.249293    2.347707
---------+--------------------------------------------------------------------
    diff |           -.1530734     .051305               -.2537514   -.0523954
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -2.9836
Ho: diff = 0                                     degrees of freedom =      998

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0015         Pr(|T| > |t|) = 0.0029          Pr(T > t) = 0.9985

. 
. * Higher for "satisficers" 
. gen conthink_sat = .
(1,002 missing values generated)

. replace conthink_sat = 0 if conthink_sd == 0
(243 real changes made)

. replace conthink_sat = 1 if conthink_sd > 0
(759 real changes made)

. ttest conthink, by(conthink_sat)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     243    3.662551    .0767421    1.196291    3.511384    3.813719
       1 |     759    3.379776    .0302072    .8322071    3.320476    3.439076
---------+--------------------------------------------------------------------
combined |   1,002    3.448353    .0297214    .9408133     3.39003    3.506677
---------+--------------------------------------------------------------------
    diff |            .2827754    .0688007                .1477651    .4177857
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   4.1101
Ho: diff = 0                                     degrees of freedom =     1000

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. 
. * Overall satisficing measure
. egen satisfice = rowtotal(conthink_sat powerless_sat anxiety_sat ///
>         egocentric_sat systemic_sat raceresent_sat whiteidentity_sat ///
>         populism_sat authoritarian_sat)

. replace satisfice = 9 - satisfice       
(1,002 real changes made)

. 
. pwcorr violence satisfice, sig          

             | violence satisf~e
-------------+------------------
    violence |   1.0000 
             |
             |
   satisfice |   0.0368   1.0000 
             |   0.2441
             |

. pwcorr justified satisfice, sig                 

             | justif~d satisf~e
-------------+------------------
   justified |   1.0000 
             |
             |
   satisfice |   0.0154   1.0000 
             |   0.6284
             |

.         
. 
end of do-file

. log close
      name:  <unnamed>
       log:  C:\Users\miles\Dropbox\Correlates of Political Violence\Replication Files\log.log
  log type:  text
 closed on:   8 Mar 2022, 13:45:26
-------------------------------------------------------------------------------------------------------------------------------------
